RRAM-Based Analog Matrix Computing for Massive MIMO Signal Processing: A Review

TL;DR

This review discusses the emerging use of RRAM-based analog matrix computing for efficient signal processing in massive MIMO systems, highlighting recent advances, opportunities, and challenges for 6G wireless communication.

Contribution

It provides a comprehensive overview of recent developments in RRAM-based AMC for massive MIMO, including novel circuit architectures and potential applications in 6G.

Findings

Demonstrated DFT/IDFT computation using MVM circuits

Reviewed iterative algorithms for MIMO detection and precoding

Identified key challenges like device reliability and circuit scalability

Abstract

Resistive random-access memory (RRAM) provides an excellent platform for analog matrix computing (AMC), enabling both matrix-vector multiplication (MVM) and the solution of matrix equations through open-loop and closed-loop circuit architectures. While RRAM-based AMC has been widely explored for accelerating neural networks, its application to signal processing in massive multiple-input multiple-output (MIMO) wireless communication is rapidly emerging as a promising direction. In this Review, we summarize recent advances in applying AMC to massive MIMO, including DFT/IDFT computation for OFDM modulation and demodulation using MVM circuits; MIMO detection and precoding using MVM-based iterative algorithms; and rapid one-step solutions enabled by matrix inversion (INV) and generalized inverse (GINV) circuits. We also highlight additional opportunities, such as AMC-based compressed-sensing recovery for channel estimation and eigenvalue circuits for leakage-based precoding. Finally, we outline key challenges, including RRAM device reliability, analog circuit precision, array scalability, and data conversion bottlenecks, and discuss the opportunities for overcoming these barriers. With continued progress in device-circuit-algorithm co-design, RRAM-based AMC holds strong promise for delivering high-efficiency, high-reliability solutions to (ultra)massive MIMO signal processing in the 6G era.